A framework on the right kernel and Lattice verses scale of data for better classification in Self organising map

Abstract

The classification of huge complex datasets to extract the information needed for making informed decisions has gained a lot of importance in today's scenario due to the outspurt of data. With an increase in dataset size, the means of obtaining the desired information via data clustering techniques also became complex. This paper illustrates a framework for selecting the best combination of the Kernel function and the Lattice structure for better classification based on the size of the data. The performance of the SOM majorly depends on the choice of Kernel Function, Lattice Structures, and the size of the datasets. The types of kernel functions deployed in this study are Gaussian, Exponential, and Laplacian Kernel along with the lattice Structures such as hexagonal, Rectangular, and Square which result in nine combinations respectively. These nine combinations of the Kernel Function and the Lattice Structure have been tested on datasets ranging from small to large size using the performance metrics. A frequency table has been arrived at based on the classification of the dataset size, combinations of Kernel functions, and Lattice Structures. The results of this study infer that the combination of Gaussian Kernel -Square Lattice performs better for small-size datasets wherein the Exponential and Laplacian Kernel functions along with Hexagonal Lattice perform better for mid-size datasets, while the Laplacian Kernel – Square Lattice perform better for large size datasets.